SpikeGUI: Software for rapid interictal discharge annotation via template matching and online machine learning

Jin, Jing; Dauwels, Justin; Cash, Sydney S.; Westover, M. Brandon

doi:10.1109/embc.2014.6944608

Cited by 8 publications

(2 citation statements)

References 18 publications

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“…Template matching has been demonstrated to successfully detect neural spiking activity both on intracranial and scalp EEG recordings [19][20][21][22]. In this paper, we use a filter design framework that is akin to such a template matching filter, with the additional benefit that it takes the noise statistics into account (in this case any non-spike EEG activity) in order to maximize the SNR for the detection.…”

Section: Spike Detection Algorithmmentioning

confidence: 99%

“…Spike events are stereotypical in that spatial and temporal signature of different events are very similar (within the same patient). Algorithms that leverage this build an average spike template that is then used as a matched filter for detecting spikes [19][20][21][22]. This type of algorithm is computationally efficient due to the low number of operations required to classify a new epoch of data.…”

Section: Introductionmentioning

confidence: 99%

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Sensor selection and miniaturization limits for detection of interictal epileptiform discharges with wearable EEG

et al. 2023

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Objective: The goal of this paper is to investigate the limits of EEG sensor miniaturization in a set-up consisting of multiple galvanically isolated EEG units to record interictal epileptiform discharges, referred to as `spikes', in people with epilepsy. Approach: A dataset of high-density EEG recordings (257 channels) was used to emulate local EEG sensor units with short inter-electrode distances. A computationally efficient sensor selection and interictal spike detection algorithm was developed and used to assess the influence of the inter-electrode distance and the number of such EEG units on spike detection performance. Signal-to-noise ratio, correlation with a clinical-grade IED detector and Cohen's kappa coefficient of agreement were used to quantify performance. Bayesian statistics were used to confirm the statistical significance of the observed results. Main Results: We found that EEG recording equipment should be specifically designed to measure the small signal power at short inter-electrode distance by providing an input referred noise $<300$ nV. We also found that an inter-electrode distance of minimum 5 cm between electrodes in a setup with a minimum of two EEG units is required to obtain near equivalent performance in interictal spike detection to standard EEG. Significance: These findings provide design guidelines for miniaturizing EEG systems for long term ambulatory monitoring of interictal spikes in epilepsy patients

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Section: Spike Detection Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sensor selection and miniaturization limits for detection of interictal epileptiform discharges with wearable EEG

et al. 2023

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Rapid annotation of interictal epileptiform discharges via template matching under Dynamic Time Warping

Jing

Dauwels

Rakthanmanon

et al. 2016

Journal of Neuroscience Methods

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Background EEG interpretation relies on experts who are in short supply. There is a great need for automated pattern recognition systems to assist with interpretation. However, attempts to develop such systems have been limited by insufficient expert-annotated data. To address these issues, we developed a system named NeuroBrowser for EEG review and rapid waveform annotation. New Methods At the core of NeuroBrowser lies on ultrafast template matching under Dynamic Time Warping, which substantially accelerates the task of annotation. Results Our results demonstrate that NeuroBrowser can reduce the time required for annotation of interictal epileptiform discharges by EEG experts by 20–90%, with an average of approximately 70%. Comparison with Existing Method(s) In comparison with conventional manual EEG annotation, NeuroBrowser is able to save EEG experts approximately 70% on average of the time spent in annotating interictal epileptiform discharges. We have already extracted 19,000+ interictal epileptiform discharges from 100 patient EEG recordings. To our knowledge this represents the largest annotated database of interictal epileptiform discharges in existence. Conclusion NeuroBrowser is an integrated system for rapid waveform annotation. While the algorithm is currently tailored to annotation of interictal epileptiform discharges in scalp EEG recordings, the concepts can be easily generalized to other waveforms and signal types.

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Fast and efficient rejection of background waveforms in interictal EEG

Bagheri

Jin

Dauwels

et al. 2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Automated annotation of electroencephalograms (EEG) of epileptic patients is important in diagnosis and management of epilepsy. Epilepsy is often associated with the presence of epileptiform transients (ET) in the EEG. To develop an efficient ET detector, a vast amount of data is required to train and evaluate the performance of the detector. Interictal EEG data contains mostly background waveforms, since ETs only occur occasionally in most patients. In order to detect ETs in an automated fashion, it is meaningful to first try to eliminate most background waveforms by means of simple, fast classifiers. The remaining waveforms can in a following step be processed by more sophisticated and computationally demanding classification algorithms, such as deep learning systems. In this study, we design a cascade of simple thresholding steps to reject most background waveforms in interictal EEG, while maintaining most ETs. Several simple and quick-to-compute EEG features are chosen. By thresholding these features in consecutive steps, background waveforms are rejected sequentially. In our numerical experiments, a cascade of 10 steps is able to reject 98.65% of all background segments in the dataset, while preserving 90.6% of the ETs.

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SpikeGUI: Software for rapid interictal discharge annotation via template matching and online machine learning

Cited by 8 publications

References 18 publications

Sensor selection and miniaturization limits for detection of interictal epileptiform discharges with wearable EEG

Sensor selection and miniaturization limits for detection of interictal epileptiform discharges with wearable EEG

Rapid annotation of interictal epileptiform discharges via template matching under Dynamic Time Warping

Fast and efficient rejection of background waveforms in interictal EEG

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